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23rd International Electronics Symposium, IES 2021

作者： Fadhillah, Excel Daris Bramastagiri, Pravasta Caraka Sigit, Riyanto Sukaridhoto, Sritrusta Brahmanta, Arya Dewantara, Bima Sena Bayu Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Electronic Engineering Surabaya Indonesia Universitas HangTuah Surabaya Department of Orthodontic Surabaya Indonesia

ISBN: (纸本)9781665443463

Odontogram is a medical record for dental patient. It records the condition of patient's tooth and helps dentist to diagnose patient's tooth and to provide further treatment. Though, many dentists do not fill odontogram completely because either they are too busy to treat other patient, or they run out of odontogram form. Therefore, this research aims to build a platform that could help dentists to create, store, and analyze dental medical record called Smart Odontogram. The platform consists of cloud computing and edge computing. The cloud computing act as a server to store and analyze medical record using big data technology and deep learning, while the edge computing diagnoses the patient's tooth using intraoral camera with the help of artificial intelligence. The AI used in this platform is using deep learning method with YOLO algorithm. The test result shows that the model accuracy for diagnosing dental disease is 85.7% and tooth classification is 99.3%. The delay result for storing medical record on the cloud has average latency 5734.2ms, retrieve patient's medical record is 91.2ms, and analytic simulation time when uploading and analyze tooth image simultaneously is 1008.8ms. © 2021 IEEE.

关键词： Edge computing

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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International Conference on Wireless and Telematics (ICWT)

作者： Eki Ahmad Zaki Hamidi Arga Valen Lazuardi Muhammad Litfan Rahmansyah Salman Alfarizi Novel Bajri Rohmat Tulloh Ridha Muldina Negara Syaiful Ahdan Jupriyadi Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Telecommunication Technology School of Applied Science Telkom University Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions.

关键词： Performance evaluation 5G mobile communication Network topology Emulation computer architecture Quality of service Aerodynamics

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Biomedical Image Quality Improvement with Spatial Domains 2

Biomedical Image Quality Improvement with Spatial Domains

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2nd International Conference on Innovative and Creative Information Technology, ICITech 2021

作者： Fernando, Erick Siagian, Pandapotan School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia

ISBN: (纸本)9781728197463

Digital image processing aims to improve the quality of an original image so that it can display an image that is relatively better than the original image, so as to obtain the detailed information needed for an analysis need. Quality degradation Biomedical images in computerization usually experience changes in brightness, blur, and contrast stretching. Because of the deterioration in the image quality, doctors and patients cannot obtain the information needed for analysis, therefore the need for biomedical image processing techniques with spatial domain methods to improve the quality and details of information in the image so that it helps doctors in diagnosing. Data analysis is biomedical images like X-ray images, CT-Scan (computer Tomographic Scan) images, and USG (Ultrasound Graphic) images. A focused study on improving image quality can be done with digital spatial domain image processing techniques. Digital domain image processing is digital image processing based on the manipulation of pixel values in the image directly, in the form of point processing and mask processing. Process testing for each image for Blur level is CT-scan images with a value of 0.98436, ultrasound images with a value of 0.9875, X-ray images with a value of 0.9836. This value is blur level value get near to 1 which means the image becomes clearer. The analysis results prove that the spatial domain method can clarify the object image in this study. The biomedical image quality processing model is proven to be able to improve the quality of the image that is declining due to the digitization process and assist the user in analyzing the biomedical image. © 2021 IEEE.

关键词： Image quality

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Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

arXiv

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arXiv 2025年

作者： Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin Department of Informatics and Communication Engineering Xiamen University Fujian361102 China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun130012 China Navigation Institute Jimei University Fujian Xiamen361021 China Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 22

A Cloud-based Distributed Computing Approach for Extracting ...

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Proceedings of the 6th International Conference on Algorithms, Computing and Systems

作者： Christos Didachos Dionisis Panagiotis Kintos Manolis Fousteris Vassilis C. Gerogiannis Le Hoang Son Andreas Kanavos Department of Computer Engineering & Informatics University of Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Greece Department of Digital Systems University of Thessaly Greece VNU Information Technology Institute Vietnam National University Vietnam Department of Informatics Ionian University Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules’ chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab" approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame.

关键词： Coiled Computing Performance Computational Drug Design Dask Molecular Descriptors Chemical Big Data

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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IEEE/SICE International Symposium on System Integration

作者： Mert Bozkurtlar Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Management Istanbul Technical University Harbiye Maçka/Beşiktaş/İstanbul Türkiye Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Ayazaga Sariyer/İstanbul Türkiye Japan Society for the Promotion of Science (JSPS) at the Nakadai Lab Tokyo Institute of Technology Honda Research Institute Japan Co. Ltd Saitama Japan

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments.

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Spectro-temporally Multiplexed Reservoir Computing Based on a Multimode Fabry Perot Laser

Spectro-temporally Multiplexed Reservoir Computing Based on ...

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European Conference on Optical Communication (ECOC)

作者： M. Skontranis G. Sarantoglou A. Bogris C. Mesaritakis Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

We present numerical results from a spectro-temporal reservoir computing based on a Fabry-Perot laser. By exploiting longitudinal modes, we achieved tunable real time processing rate, reaching up to 2.38 GHz for an im... 详细信息

ISBN: (数字)9781957171159

ISBN: (纸本)9781665475570

关键词： Multiplexing Europe Reservoirs Laser modes Real-time systems Optical fiber communication Fabry-Perot

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Opportunistic Routing for Mobile Edge Computing: A Community Detected and Task Priority Aware Approach

SSRN

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SSRN 2024年

作者： Wu, Jia Dai, Tingyi Guan, Peiyuan Chen, Ziru Gou, Fangfang Taherkordi, Amir The Department of Informatics University of Oslo Oslo0316 Norway The Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States The School of Computer Science and Technology GuiZhou University Guiyang550025 China The Research Center for Artificial Intelligence Faculty of IT Monash University MelbourneVIC3800 Australia

As the 5G era develops quickly, there has been a significant growth in the amount of network data. Concurrently, traditional routing algorithms are encountering growing challenges. Traditional routing algorithms typically rely on maintaining stable end-to-end paths to facilitate smooth data transmission. However, in wireless multi-hop networks, the mobility of nodes and the dynamic nature of network topology make maintaining stable end-to-end paths exceedingly difficult, if not impossible. Consequently, data transmission efficiency decreases while network overhead increases. To address these challenges, opportunistic routing has emerged. Opportunistic routing is self-organizing networks that rely on chance encounters between nodes to transmit messages, and the store-and-carry forwarding technique is used for data transmission between nodes. Typically, most traditional opportunistic routing algorithms divide nodes into communities. However, such approaches often overlook node characteristics, potentially leading to disparate nodes being assigned to separate communities despite positive correlations. Thus, accurately dividing communities remains a significant challenge. To address this, we propose the Efficient Community Detection Data Transmitting (ECDDT) algorithm. During community division, ECDDT combines social attributes and network topology using mobile edge computing. Subsequently, to expedite task execution, we schedule these tasks in a reasonable manner. Through simulation experiments across four diverse real datasets, we evaluate the algorithm's efficacy. Our findings demonstrate favorable performance in delivery rate and routing overhead. © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing

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Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction*

Multi-View 2D to 3D Lifting Video-Based Optimization: A Robu...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Daniela Rato Miguel Oliveira Vítor Santos Angel Sappa Bogdan Raducanu Department of Mechanical Engineering Institute of Electronics and Informatics Engineering of Aveiro Campus Universitário de Santiago University of Aveiro Aveiro Portugal LASI - Intelligent System Associate Laboratory Portugal Edificio O Computer Vision Center Barcelona Spain ESPOL Polytechnic University Guayaquil Ecuador

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the context of robotics, accurate 3D human pose estimation is essential for enhancing human-robot collaboration and interaction. This manuscript introduces a multi-view 2D to 3D lifting optimization-based method designed for video-based 3D human pose estimation, incorporating temporal information. Our technique addresses key challenges, namely robustness to 2D joint detection error, occlusions, and varying camera perspectives. We evaluate the performance of the algorithm through extensive experiments on the MPI-INF-3DHP dataset. Our method demonstrates very good robustness up to 25 pixels of 2D joint error and shows resilience in scenarios involving several occluded joints. Comparative analyses against existing 2D to 3D lifting and multi-view methods showcase good performance of our approach.

关键词： Three-dimensional displays Accuracy Pose estimation Robot vision systems Human-robot interaction Cameras Robustness Intelligent robots Resilience

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TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning

arXiv

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arXiv 2022年

作者： Le, Tien Thanh Ji, Yusheng Lui, John C.S. Department of Informatics The Graduate University for Advanced Studies SOKENDAI Tokyo Japan National Institute of Informatics Tokyo Japan Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims. © 2022, CC BY.

关键词： Reinforcement learning

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